System Exploration for Enhanced Optionally-Crewed Platforms (SENATOR)
Abstract
The current growth of crewless platforms (unmanned) in the maritime domain is revealing challenges not yet encountered in other domains. Unlike air and land platforms, marine platforms need to be capable of multi-month deployments. The timescale of these deployments mean the platform must be capable of sensing its current state, maintaining itself, changing operations as its capability degrades, and performing long-term mission planning and sustainment tasks to optimize its effectiveness. Such tasks are not required for other forms of crewless systems that have mission duration of hours to days, or systems where only mission execution is automated, not maintenance and planning. The success of future crewless platforms requires basic research exploration of the corresponding supporting algorithms for the unique challenges of the naval space. These challenges can be divided into three categories of risk: risk of mission failure, risk of sustainment failures, and risk of inflexibility for future upgrades. Such algorithms must be capable of fusing multiple uncertain data inputs with physics-based models to optimally assess platform status and plan future platform operations. Furthermore, it is unlikely that successful algorithms alone will be sufficient to completely replace human crews on platforms with today~s physical and functional architectures. Thus, a critical emerging challenge is that of codesigning the new algorithms, plat-form architecture, sensors, and sustainment to work together, such that the areas difficult to solve algorithmically are covered by other functional or physical modifications. This proposal addresses initial exploration of such algorithms and architectures for long-mission surface and sub-surface platforms through a series of functional investigations, algorithmic explorations, and finally the development of a plan for an integrated model-scale experimental demonstration.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 17, 2020
- Source ID
- N000142012044
Entities
People
- Matthew Collette
Organizations
- Board of Regents of the University of Michigan
- Office of Naval Research
- United States Navy